Recent technological developments have resulted in a wealth of genetic sequence, genomic, expression, proteomic and other omic data that will potentially transform our understanding of the architecture of complex traits and diseases. The complexity of the problem to use and understand all this data in human subjects, however, has resulted in the rate of development of statistical and methodological tools lagging behind the rate of influx of new data. The long term goal of this project is to develop analytical and computational methods that use these data to build statistical models that will lead to the pinpointing of individual variants, identification o networks of interacting genes, and understanding of how sets of measurable biological variables leads to realized quantitative and disease phenotypes. We focus our methodological development on studies of isolated populations, where for historic or demographic reasons the sampled population tends to be relatively genetically homogenous and where many of the individuals may be related, possibly cryptically. The relative lack of heterogeneity of isolated populations is advantageous for the discovery of genetic variants because genetic effects will tend to concentrate into fewer loci, and some of the variants that in the larger population are rare will be more frequent, aiding in their discovery. Furthermore, isolated populations often live in a more uniform environment, reducing this as a confounding effect. The unexpected correlations that arise, however, due to both recent and distant relatedness, provides both methodological challenges and the opportunity to use this information to obtain greater biological insight. In pursuit of the above stated goal, then, we will develop methods for use in isolated populations that use large genomic and other data sets that will lead to a greater understanding of the biological underpinnings of complex traits. The specific aims of this proposal are (1) to develop methods for estimating identity by descent (IBD) using sequence data, (2) to develop methods for whole-genome complex trait mapping using IBD, (3) to develop methods for whole-genome association mapping using all observed variants simultaneously, and (4) to develop methods using multiple omics data sets to create comprehensive models for complex traits. In addition to developing the statistical methods we will implement them in computationally efficient open-source software packages. These packages will be promptly made available to the wider genetic analysis community.